SPP1 as diagnostic marker for sepsis encephalopathy, application, kit and composition

Abstract

The invention discloses a diagnostic marker for sepsis encephalopathy, application, a kit and a composition. Specifically, whether the expression level of the Spp1 is normal or not is judged by detecting the expression level of the Spp1 gene or the expression level of the protein so as to assist in diagnosing the sepsis encephalopathy.

Description

Technical Field:

[0001] The present invention belongs to the field of pharmaceutical technology. Specifically, the present invention relates to plasma macrophage-derived SPP1 as a diagnostic marker, application, kit and composition for sepsis encephalopathy. Background Art:

[0002] Sepsis-associated encephalopathy (SAE) is manifested as diffuse brain dysfunction without direct central nervous system infection. Its clinical symptoms include changes in consciousness (delirium, lethargy, coma), attention deficit, decline in executive function and memory impairment, etc., which have a significant impact on the prognosis and long-term quality of life of patients. Epidemiological investigations show that in the intensive care unit (ICU), about 50%-70% of sepsis patients will experience varying degrees of cognitive impairment, and 30% of patients will develop irreversible cognitive impairment. The clinical prevalence of sepsis encephalopathy and its secondary related brain injury has been widely recognized, but the pathogenesis is highly complex and involves multi-system and multi-level pathophysiological processes. Its core mechanisms mainly include key links such as systemic inflammatory response, blood-brain barrier disruption, neurotransmitter imbalance and immune cell infiltration. During the process of blood-brain barrier damage, microglia are abnormally activated, releasing a large amount of reactive oxygen species (ROS) and inflammatory mediators, forming a positive feedback loop of "inflammation-oxidative stress". This process directly leads to apoptosis of neurons in the hippocampal region and causes synaptic plasticity damage; at the same time, a significant decrease in acetylcholine levels is significantly correlated with the severity of cognitive impairment. The mechanism lies in that inflammatory factors inhibit the activity of choline acetyltransferase, disrupt the normal function of cholinergic neurotransmission, and at the same time enhance the excitotoxicity of glutamate, ultimately leading to the disorder of neuronal network function. It should be noted that a variety of immune cells play a key role in the occurrence and development of SAE, among which regulatory T cells, neutrophils and macrophages, etc. participate in this pathological process through a complex regulatory network. These mechanisms are intertwined and interact with each other, jointly constituting the multi-dimensional pathogenesis network of SAE.

[0003] Although existing studies have clarified some of the mechanisms of SAE, many key issues remain inadequately answered. From a pathological perspective, the development of this SAE involves complex interactions among multiple systems and various cells, yet the research on its overall regulatory network remains blank. In terms of the "peripheral - central" axis, the pathways by which peripheral inflammatory processes affect the central nervous system have not been fully elucidated, especially the specific mechanisms of action of peripheral immune cells in this pathological process still need to be further explored. In clinical practice, the diagnosis and treatment of SAE face a dual dilemma: there is a lack of specific biomarkers, effective treatment strategies are lacking, and significant technical difficulties also exist in the prognostic assessment of patients. As a multifunctional extracellular matrix protein, SPP1 plays an important regulatory role in innate immune responses and intercellular signal transduction processes through receptor systems such as integrin and CD44. Summary of the Invention:

[0004] The present invention determines whether the expression level of Spp1 is normal by detecting the expression level of the Spp1 gene or protein expression level, so as to assist in the diagnosis of sepsis encephalopathy.

[0005] The present invention provides a biomarker for detecting sepsis encephalopathy, characterized in that the biomarker is SPP1.

[0006] In one embodiment, the application of the biomarker for sepsis encephalopathy in the preparation of a product for diagnosing sepsis encephalopathy.

[0007] In one embodiment, the present invention provides a method for detecting a biomarker for sepsis encephalopathy, characterized in that it detects the expression level of Spp1 in peripheral macrophages, detects the expression level of SPP1 in plasma, and / or detects the transcriptional level of the Spp1 gene.

[0008] In one embodiment, the detection method provided by the present invention is characterized in that the protein expression level of SPP1 is directly detected by ELISA, immunoblotting or immunohistochemistry methods, or the transcriptional level of the Spp1 gene is detected by qRT-PCR to evaluate the expression level of Spp1.

[0009] The present invention also provides the application of a Spp1 gene, an antibody that inhibits or binds to the SPP1 protein in the preparation of a drug for treating sepsis encephalopathy.

[0010] In one embodiment, the application described in the present invention is characterized in that the drug treats sepsis encephalopathy by reducing the phagocytosis of neuronal synaptic proteins by microglia.

[0011] In one embodiment, the application of the present invention is characterized in that the degree of microglial activity is reduced by reducing the transfer of peripheral SPP1 protein into microglial cells in the hippocampal region of the brain.

[0012] In one embodiment, the application of the present invention, wherein the inhibitor is at least one of a specific miRNA, RNAi, ribozyme targeting the Spp1 gene or a small molecule inhibitor targeting the SPP1 protein.

[0013] The present invention also provides a pharmaceutical composition, characterized in that the pharmaceutical composition comprises an antibody binding to the SPP1 protein, SPP1 inhibitory, and the Spp1 gene.

[0014] In one embodiment, the pharmaceutical composition of the present invention is characterized in that the pharmaceutical composition further comprises a pharmaceutically acceptable excipient; preferably, the excipient comprises any one or a combination of at least two of a carrier, a diluent, an emulsifier, a solubilizer, a solubilizing agent, an osmotic pressure regulator, a coating material, a colorant, a pH regulator, an antioxidant, a bacteriostatic agent or a buffer.

[0015] Terms and definitions:

[0016] Prevention and treatment

[0017] As used herein, "prevention" refers to all actions for deterring or postponing the onset of a neurodegenerative disease by administering a medicament according to the present invention, and "treatment" refers to all actions for improving or favorably altering the symptoms of a subject having or suspected of having a neurodegenerative disease by administering a medicament. Detection of expression levels Detection of the expression level of a gene encoding a protein is performed using conventional molecular biological means and generally falls into two categories. The first category is the detection of the mRNA level of the gene. The second category is the detection of the protein, the product obtained after the expression of the gene. Overexpression or high expression means a protein or nucleic acid that is transcribed or translated at a detectably higher level in a cell relative to a normal cell. The term includes overexpression caused by transcription, post-transcriptional processing, translation, post-translational processing, cellular localization (e.g., organelle, cytoplasm, nucleus, cell surface), and RNA and protein stability (relative to normal cells). Conventional techniques for detecting mRNA (i.e., RT-PCR, PCR, hybridization) or proteins (i.e., ELISA, immunohistochemistry techniques) can be used to detect overexpression. The overexpression can be 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher than that of normal cells. In some examples, the overexpression is a transcriptional or translational level that is 1-fold, 2-fold, 3-fold, 4-fold or higher relative to normal cells. Low expression means a protein or nucleic acid that is transcribed or translated at a detectably lower level in a cell relative to a normal cell. The term includes low expression caused by transcription, post-transcriptional processing, translation, post-translational processing, cellular localization (e.g., organelle, cytoplasm, nucleus, cell surface), and RNA and protein stability (relative to a control). Conventional techniques for detecting mRNA (i.e., RT-PCR, PCR, hybridization) or proteins (i.e., ELISA, immunohistochemistry techniques) can be used to detect low expression. The low expression can be 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or lower than the control. In some examples, the low expression is a transcriptional or translational level that is 1-fold, 2-fold, 3-fold, 4-fold or lower relative to a control. Differential expression generally means that a protein or nucleic acid in one sample is overexpressed (upregulated) or underexpressed (downregulated) relative to at least one other sample. For the purposes of the present invention, a sample from an individual suspected of having a neurodegenerative disorder is typically compared to a sample from an individual known to have the disorder (positive control) or known to be negative for the disorder (negative control).

[0018] "Marker" or "detectable moiety" is a component that can be detected by spectrophotometry, photochemistry, biochemistry, immunochemistry, chemistry, or other physical methods. For example, useful markers include 32P, fluorescent dyes, reagents with high electron density, enzymes (such as those commonly used in ELISA), biotin, digoxigenin, or haptens and proteins that can be made detectable (such as by incorporating a radioactive label into a peptide) or used to detect antibodies that specifically react with a peptide. The marker can be conjugated to a targeting molecule, such as an antibody or a nucleotide sequence for specifically detecting a target compound.

[0019] "Inhibitor" is a compound that, for example, binds to, partially blocks or completely blocks activity, reduces, prevents, delays activation, inactivates, desensitizes, or downregulates the activity or expression of a neurodegeneration biomarker. Inhibitors, activators or modulators also include genetically modified forms of neurodegeneration biomarkers, such as forms with altered activity, as well as natural and synthetic ligands, antagonists, agonists, antibodies, peptides, cyclic peptides, nucleic acids, antisense molecules, ribozymes, RNAi and siRNA molecules, small organic molecules, etc.

[0020] Nucleic acid, nucleotide or polynucleotide

[0021] "Nucleic acid", "nucleotide" or "polynucleotide" refers to deoxyribonucleotides or ribonucleotides, and polymers in their single-stranded or double-stranded forms, and their complements. The term includes nucleic acids containing known nucleotide analogs or modified backbone residues or linkages, which are synthetic, natural and non-natural, which have similar binding characteristics to a reference nucleic acid, and which are metabolized in a manner similar to a reference nucleotide. Examples of such analogs include, but are not limited to, phosphorothioates, phosphoroamidates, methylphosphonates, chiral methylphosphonates, 2-O-methyl ribonucleotides, peptide nucleic acids (PNA).

[0022] Small RNA refers to non-coding RNA, which is generally less than about 200 nucleotides in length or less and has a silencing or interfering function. In other embodiments, the small RNA is about 175 nucleotides or shorter, about 150 nucleotides or shorter, about 125 nucleotides or shorter, about 100 nucleotides or shorter, or about 75 nucleotides or shorter. Such RNAs include microRNA (miRNA), small interfering RNA (siRNA), double-stranded RNA (dsRNA), and short hairpin RNA (shRNA). "Small RNA" of the present disclosure should be capable of inhibiting or knocking down the gene expression of a target gene, typically through a pathway that results in the destruction of the target gene mRNA.

[0023] Amino acid, peptide, polypeptide, protein

[0024] "Polypeptide", "peptide", and "protein" are used interchangeably herein and refer to polymers of amino acid residues. This

[0025] term applies to amino acid polymers in which one or more amino acid residues are artificial chemical mimics of the corresponding naturally occurring amino acids, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers.

[0026] Amino acids refer to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that act in a manner similar to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those that have been subsequently modified, such as hydroxyproline, γ-carboxyglutamic acid, and O-phosphoserine. Amino acid analogs are compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., a carbon bonded to hydrogen, a carboxyl group, an amino group, and an R group, such as homoserine, norleucine, methionine sulfoxide, and methionine methyl sulfonium. Such analogs have a modified R group (e.g., norleucine) or a modified peptide backbone, but retain the same basic chemical structure as the naturally occurring amino acid. Amino acid mimetics are chemical compounds that have a structure different from the general chemical structure of an amino acid, but act in a manner similar to a naturally occurring amino acid.

[0027] Antibody

[0028] An antibody refers to a polypeptide that comprises a framework region or a fragment thereof from an immunoglobulin gene that specifically recognizes and binds an antigen. The recognized immunoglobulin genes include the κ, λ, α, γ, δ, ε, and μ constant region genes, as well as numerous immunoglobulin variable region genes. Light chains are classified as κ or λ. Heavy chains are classified as γ, μ, α, δ, or ε, which in turn define the immunoglobulin class, namely IgG, IgM, IgA, IgD, and IgE, respectively. Generally, the antigen-binding region of an antibody is the most important for binding specificity and affinity. Antibodies can be polyclonal or monoclonal, derived from serum, hybridomas, or recombinant clones, and can also be chimeric, primatized, or humanized.

[0029] Exemplary immunoglobulin (antibody) structural units include tetramers. Each tetramer is composed of two pairs of identical polypeptide chains, each pair having one "light chain" (about 25 kDa) and one "heavy chain" (about 50 - 70 kDa). The N-terminus of each chain defines a variable region of approximately 100 to 110 or more amino acids, which is mainly responsible for antigen recognition. The terms light chain variable region (VL) and heavy chain variable region (VH) refer to these light and heavy chains, respectively.

[0030] Antibodies exist, for example, in the form of intact immunoglobulins or in the form of various well-characterized fragments produced by digestion with various proteases. Thus, for example, pepsin digests an antibody below the disulfide bonds in the hinge region, thereby producing F(ab)’2, which is a dimer of Fab, and Fab itself is a light chain linked to VH-CH1 by a disulfide bond. F(ab)’2 can be reduced under mild conditions to break the disulfide bonds in the hinge region, thereby converting the F(ab)’2 dimer into Fab’ monomers. The Fab’ monomers are essentially Fab with a portion of the hinge region (see Fundamental Immunology (Paul ed., 3d ed. 1993)). Although various antibody fragments are defined by digestion of intact antibodies, those skilled in the art will recognize that such fragments can be synthesized de novo by chemical means or by using recombinant DNA methods. Thus, the term antibody as used herein also includes antibody fragments produced by modification of whole antibodies, or those synthesized de novo using recombinant DNA methods (such as single-chain Fv) or those identified using phage display libraries (see, e.g., McCafferty et al., Nature 348:552-554 (1990)).

[0031] As used herein, "antibody" can also refer to any functional VH and VL pair (i.e., capable of specifically binding an epitope) that are each linked in various configurations to other polypeptides that can perform various functions, such as receptors, receptor inhibitors, or stabilizers of the VH-VL complex.

[0032] When referring to a protein, nucleic acid, antibody, or small molecule compound, the term "specifically (or selectively) binds" refers to a binding reaction that typically measures the presence of a protein or nucleic acid (e.g., STAT3, TYK2, or a modified form thereof) in a heterogeneous population of proteins or nucleic acids and other biological agents. In the case of an antibody, under specified immunoassay conditions, the binding of a specified antibody to a particular protein can be at least 2-fold over background, more typically 10-fold or more up to 100-fold over background. Specific binding to an antibody under such conditions requires those antibodies that have been selected for their selectivity for a particular protein. For example, polyclonal antibodies can be screened to obtain only those polyclonal antibodies that have a specific immune reaction with the selected antigen and no specific immune reaction with other proteins. Such screening can be achieved by subtracting antibodies that cross-react with other molecules. A variety of immunoassay formats can be used to select antibodies that have a specific immune reaction with a particular protein. For example, solid-phase ELISA immunoassays are routinely used to select antibodies that have a specific immune reaction with a protein (see, e.g., Harlow & Lane, Antibodies, A Laboratory Manual (1988), which describes immunoassay formats and conditions that can be used to measure specific immunoreactivity). Nucleotide-based assays

[0033] In some embodiments, RNA from a biological sample is used to detect the expression of Spp1 by real-time or quantitative PCR. RNA can be extracted by any method known to those of skill in the art, such as using Trizol and RNeasy. Real-time PCR can be performed by any method known to those of skill in the art, such as Taqman real-time PCR using an Applied Biosystem assay. Gene expression is calculated relative to pooled normal lung RNA and the expression is corrected for a housekeeping gene. Appropriate oligonucleotide primers are selected by those of skill in the art.

[0034] In one embodiment, nucleic acid binding molecules such as probes, oligonucleotides, oligonucleotide arrays, and primers are used to detect RNA biomarkers to detect differential RNA expression in a patient sample. In one embodiment, RT-PCR is used according to standard methods known in the art. In another embodiment, quantitative PCR assays (such as assays available from, for example, Applied Biosystems) can be used to detect nucleic acids and their variants. In other embodiments, nucleic acid microarrays can be used to detect nucleic acids. Analysis of nucleic acids can be achieved using conventional techniques such as northern analysis, or any other method based on hybridization with a nucleic acid sequence (which is complementary to a portion of the biomarker coding sequence) (such as strip blot hybridization) is also included within the scope of the present invention. Reagents that bind to a selected nucleic acid biomarker can be prepared according to methods known to those skilled in the art or purchased from commercial sources. Description of the Drawings:

[0035] Figure 1 : SAE mice showed a decline in cognitive level in the behavioral test on the 7th day

[0036] Figure 2 : SAE mice showed a decline in cognitive level in the behavioral test on the 14th day

[0037] Figure 3 : Transcriptome characterization analysis of the brain tissue of SAE mice;

[0038] Figure 4 : Results of co-expression network analysis of SAE genes;

[0039] Figure 5 : Intersection analysis of WGCNA analysis of SAE-related genes and DEG characteristic gene sets;

[0040] Figure 6 : Screening of key genes for SAE by machine learning;

[0041] Figure 7 : Identification of the intersection of the candidate gene Spp1 and evaluation of its diagnostic efficacy in SAE mice;

[0042] Figure 8 : Expression levels of central and peripheral Spp1 mRNA genes in SAE mice;

[0043] Figure 9 : Immunofluorescence analysis of SPP1 protein expression in the hippocampus of SAE mice after CLP;

[0044] Figure 10 : Temporal change pattern of SPP1 protein expression in the hippocampus of SAE mice;

[0045] Figure 11 : Co-immunostaining of SPP1 protein and vascular endothelial CD31 in the hippocampus of SAE mice;

[0046] Figure 12 : Tracer experiment to verify the transport of peripheral SPP1 to the central nervous system;

[0047] Figure 13 : Conditional knockout of Spp1 in peripheral macrophages on day 7 improves SAE;

[0048] Figure 14 : Conditional knockout of Spp1 in peripheral macrophages on day 14 improves sepsis-associated cognitive impairment;

[0049] Figure 15 : Conditional knockout of Spp1 in peripheral macrophages reduces the expression of SPP1 protein in the hippocampal region. Specific implementation method:

[0050] 1. Construction of SAE mouse model

[0051] (1) Behavioral phenotype analysis of SAE mice

[0052] In this study, cecal ligation and puncture (CLP) mice (CLP group, n = 12) were used, and a sham operation control group (Sham group, n = 9) was established. To comprehensively evaluate the impact of SAE on the neurocognitive function of mice, the research team conducted behavioral tests at two time points, the 7th day and the 14th day after surgery. The statistical significance level was set at P < 0.05.

[0053] 1) Behavioral analysis on the 7th day after surgery

[0054] A. Open field test (OFT) to verify motor ability

[0055] The OFT results showed that there was no significant difference in the total moving distance between the CLP group and the Sham group (CLP group: 2013.22 ± 378.23 cm vs Sham group: 2280.30 ± 333.67 cm; t = 1.04, P = 0.3124, Figure 1 A and B), which indicated that the CLP surgery did not have a significant impact on the basic motor function of mice during the experimental observation period. It effectively excluded the possible interference factors of motor ability in subsequent cognitive function tests and provided a reliable prerequisite for accurately evaluating cognitive function changes.

[0056] B. Novel object recognition test (NOR) to detect short-term memory

[0057] Novel object exploration time: The novel object exploration time of mice in the CLP group was significantly lower than that in the Sham group (1.19 ± 2.08 s vs 4.03 ± 2.85 s, t = 2.41, P = 0.0263); Recognition index (RI): The recognition index (RI) of the CLP group decreased significantly compared with the Sham group, from 77.7% ± 14.92% to 26.45% ± 30.45% (t = 3.87, P = 0.0010, Figure 1 C and D). Behavioral observations showed that mice in the CLP group not only exhibited a significant reduction in exploratory behavior but also showed abnormal locomotor patterns. These behavioral changes indicated that the spatial exploration motivation of mice in the CLP group was significantly weakened, and their cognitive flexibility was also significantly impaired. These experimental results suggested that CLP treatment might have a significant negative impact on the spatial cognitive function and exploratory behavior of mice.

[0058] C. Y-maze experiment (YM) to evaluate spatial working memory

[0059] Total number of entries into arms: The CLP group (34 ± 13.37 times) was significantly less than the Sham group (48 ± 8.69 times) (t = 2.41, P = 0.0209, Figure 3 .2.3 - 5E - F); Spontaneous alternation rate: The CLP group was 55.55 ± 6.7%, which was also significantly lower than that of the Sham group (64.59 ± 5.33%) (t = 2.54, P = 0.0198, Figure 1 E and F). The above results indicated that on the 7th day after CLP, the experimental mice showed obvious impairment of spatial memory function.

[0060] Figure 1 A. Representative diagram of the movement trajectory of mice in the OFT on the 7th day after CLP modeling. B. Histogram of the total movement distance of mice in the OFT on the 7th day after CLP modeling. C. Representative diagram of the movement trajectory of mice in the NOR test chamber on the 7th day after CLP modeling. D. Novel object exploration time and novel object recognition index of mice tested in the NOR chamber on the 7th day after CLP modeling. E. Representative diagram of the movement trajectory of mice in the YM experiment on the 7th day after CLP modeling. F. Histogram of the spontaneous alternation rate and total number of entries into arms of mice in the YM chamber on the 7th day after CLP modeling. Sham group: Sham operation control group; M - CLP group: Moderate sepsis group. N = 9 in the Sham group, N = 12 in the M - CLP group, and unpaired t - test was used for comparison between groups. ns: not significant, *P < 0.05, **P < 0.01.

[0061] 2) Behavioral analysis on the 14th day after surgery

[0062] A. Open field test (OFT) to verify motor ability

[0063] The OFT results showed that there was no significant difference in the total moving distance between the CLP group and the Sham group (CLP group: 2166.74 ± 189.19 cm vs Sham group: 2,555.49 ± 749.33 cm; t = 1.04, P = 0.7735, Figure 2 A B). This finding suggests that the CLP surgery did not have a significant impact on the basic motor function of mice during the experimental observation period. It effectively excluded the possible interference factors of motor ability in the subsequent cognitive function tests, providing a reliable prerequisite for accurately evaluating the changes in cognitive function.

[0064] B. Novel object recognition experiment (NOR) to verify long-term memory impairment

[0065] The exploration time of the novel object: The CLP group (1.35 ± 0.48 s) was significantly lower than that of the Sham group (2.24 ± 0.50 s) (t = 3.52, P = 0.0031); Recognition index (RI): The RI value of the CLP group (32.74 ± 9.58)% was significantly lower than that of the Sham group (46.29 ± 11.18)% (t = 2.71, P = 0.0161, Figure 2 C and D). It is suggested that moderate sepsis has a persistent impairment on the long-term novel object recognition ability of mice.

[0066] C. Y-maze experiment (YM) to evaluate long-term spatial memory

[0067] The total number of entries into the arms: The CLP group (30.33 ± 4.65 times) was significantly less than that of the Sham group (38.25 ± 6.19 times) (t = 2.41, P = 0.0292, Figure 2 E and F); The spontaneous alternation rate: The CLP group (63.05 ± 4.84)% was also significantly lower than that of the Sham group (69.61 ± 3.40)% (t = 2.82, P = 0.013, Figure 2 E and F). These results suggest that on the 14th day after CLP surgery, the experimental mice still showed obvious spatial memory function impairment, and this finding further verified that the SAE model can better simulate the characteristics of long-term cognitive dysfunction.

[0068] Figure 2A. Representative locomotion trajectories of mice in the OFT on the 14th day after modeling. B. Bar graph of the total locomotion distance of mice in the OFT on the 14th day after modeling. C. Representative locomotion trajectories of mice tested in the NOR experimental chamber on the 14th day after modeling. D. Exploration time of novel objects and novel object recognition index of mice tested in the NOR chamber on the 14th day after modeling. E. Representative locomotion trajectories of mice in the YM experimental chamber on the 14th day after modeling. F. Bar graph of the spontaneous alternation rate and total number of arm entries of mice in the YM chamber on the 14th day after modeling. Sham group: Sham operation control group; M-CLP group: Moderate sepsis group. N = 8 in the Sham group and N = 9 in the M-CLP group. Unpaired t-test was used for comparison between groups. *P < 0.05, **P < 0.01

[0069] 2. Screening and identification of genes closely related to post-sepsis cognitive dysfunction

[0070] (1) Joint analysis of differentially expressed genes in the brain tissues of SAE mice

[0071] 1) Global analysis of differentially expressed genes in the brain tissues of SAE mice:

[0072] In this study, through comprehensive analysis of 16 samples (8 cases in each of the CLP group and the Sham group) in the public transcriptome databases (GSE198861, GSE167610), and applying standardized quality control and differential expression screening (|log2FC| > 1, P-value < 0.05), a total of 6,457 differentially expressed genes (DEGs) were identified, including 84 significantly differentially expressed genes. The volcano plot clearly shows the distribution characteristics of gene expression changes: 75 significantly up-regulated genes are marked in red (accounting for 89.3%), 9 significantly down-regulated genes are marked in green (accounting for 10.7%), and genes that did not reach the significance threshold are shown in gray (n = 6,373)( Figure 3 A). This result indicates that under the pathological state of SAE, the transcriptional regulation of brain tissues shows an obvious unidirectional activation trend.

[0073] 2) Cluster expression patterns of differentially expressed genes:

[0074] Heatmap constructed based on hierarchical clustering analysis( Figure 3B) Further verified the expression characteristics of differentially expressed genes. Row clustering analysis showed that 84 significantly differentially expressed genes presented a clear clustering separation pattern between the CLP group and the Sham group, indicating significant differences in gene expression levels between the two groups of samples. Column clustering analysis results showed that the gene expression patterns within the groups had a high degree of consistency, and no obvious batch effect interference was detected, ensuring the reliability of the experimental data. Specifically, in the CLP group, 75 upregulated genes generally showed high expression characteristics (represented by dark red regions), while the expression levels of 9 downregulated genes were significantly higher in the control group than in the CLP group (represented by dark green regions). This differential expression pattern was highly consistent with the previous volcano plot analysis results, not only verifying the effectiveness of the differentially expressed gene screening criteria but also further confirming the reliability of the experimental results.

[0075] 3) Screening of core hub genes by MCC algorithm:

[0076] In this study, a protein - protein interaction (PPI) network was constructed based on the STRING database (confidence score > 0.7), which contained 84 nodes and 326 interaction edges ( Figure 3 C). Using the CytoHubba plugin of the Cytoscape platform for network topology analysis, 10 core hub genes were screened out by the MCC (Matthews Correlation Coefficient) algorithm, including Pecam1 (Degree = 13), Icam1 (13), Vwf (12), Agt (11), Spp1 (10), Lcn2 (10), Cd14 (9), Nfkbia (9), Socs3 (8) and Sparc (7). Gene function analysis showed that these core genes were mainly involved in multiple important biological processes: Icam1, Spp1 and Cd14 played key roles in the inflammatory response; Pecam1 and Vwf were involved in vascular regulation; Socs3 and Nfkbia mediated signal transduction. Notably, 7 out of 10 core genes (accounting for 70%) were closely related to the innate immune response, and this finding further confirmed the core position of neuroinflammation in the pathogenesis of SAE.

[0077] Among them Figure 3A. Volcano plot of differentially expressed genes: The horizontal axis represents log2FC (fold change in gene expression), and the vertical axis represents -log10(p-value) (significance). Red dots: significantly upregulated genes; green dots: significantly downregulated genes; black dots: genes with no significant change. B. Heatmap of differential genes: Row-normalized expression values, color gradient: red indicates high expression, green indicates low expression, and the depth of color represents the level of expression. C. PPI network diagram: Node size: positively correlated with connectivity, indicating the intensity of gene interaction; edge color: reflects the STRING interaction score intensity, and the darker the color, the higher the score.

[0078] (2) Joint analysis of transcriptome data of SAE mouse brain tissue based on WGCNA: Gene co-expression patterns and identification of differential genes

[0079] 1) Construction of WGCNA co-expression network and module identification:

[0080] In this study, two public datasets, GSE198861 and GSE167610, were integrated, and weighted gene co-expression network analysis (WGCNA) technology was used to systematically analyze the transcriptome data of the brain tissue of the SAE mouse model. In the data preprocessing stage, the research team standardized the expression profiles of 6,457 genes. In subsequent analyses, the dynamic shear tree algorithm was used to perform hierarchical clustering on the gene expression data, and the topological overlap matrix (TOM) was used as a quantitative indicator of co-expression similarity between genes. Through the average linkage clustering method, a complete gene dendrogram ( Figure 4 A) was generated. The research applied the dynamic module merging algorithm, set the height threshold for module merging to 0.25, and finally successfully identified 8 gene modules with significant co-expression characteristics, named MEtan, MEsalmon, MEgreenyellow, MElightcyan, MEmagenta, MEmidnightblue, MEgreen, and MEgrey ( Figure 4 B). These modules showed significant differences in the number of genes. Among them, the MEmidnightblue module contained 3,429 genes, which was the largest module; while the MEtan module contained 205 genes, but was identified as one of the modules with key biological functions.

[0081] 2) Correlation analysis between modules and phenotypic characteristics:

[0082] To deeply analyze the association mechanism between gene modules and phenotypic characteristics, this study used the Pearson correlation analysis method to systematically evaluate the correlation of module eigengenes between the two phenotypic characteristics of Sham and CLP. The study conducted a comprehensive correlation analysis on the 8 key modules identified (MEtan, Mesalmon, MEgreenyellow, MElightcyan, MEmagenta, MEmidnightblue, MEgreen, MEgrey). Figure 4 B). The results showed that the MEtan module showed significant negative and positive correlations in the Sham and CLP phenotypic characteristics respectively, with a correlation coefficient of 0.88 (P = 7e -06 ), and this module contains 205 characteristic genes. The MEmidnightblue module also showed significant correlations in the two phenotypic characteristics, with correlation coefficients of 0.82 and -0.82 respectively (P = 9e -05 ), and this module contains 3429 characteristic genes. These two modules showed highly significant correlations with the phenotypic characteristics, suggesting that the MEtan and MEmidnightblue modules may play important regulatory roles in the pathophysiological process of SAE and are worthy of further in-depth study of their molecular mechanisms.

[0083] 3) Distribution characteristics of gene significance within the module:

[0084] Furthermore, the association strength between genes in each module and the SAE phenotype was evaluated by gene significance (GS). The analysis results showed that there were significant differences in the gene significance distribution between the MEtan and MEmidnightblue modules, and their significance levels were significantly higher than those of other modules (P < 0.001). Specifically, the gene significance distribution of the MEtan module showed a unique bimodal characteristic, and this distribution pattern indicates that this module may contain multiple gene subgroups with different functions, and these subgroups may play specific biological functions in the occurrence of SAE. On the other hand, the gene significance distribution of the MEmidnightblue module showed a typical unimodal skewed characteristic, and this distribution pattern suggests that there may be a co-regulation mechanism among the genes in this module and they jointly participate in the regulation of specific biological pathways. It is worth noting that the mean gene significance of these two modules both exceeded 0.6, further verifying their important biological roles in SAE. These research results provide important experimental basis and theoretical directions for further in-depth analysis of the molecular mechanism of SAE and have important reference value for clarifying the pathophysiological process of SAE. Figure 4 C).

[0085] 4) Co - verification of the synergy between module members and gene significance:

[0086] In the study of co - verification of the synergy between module members and gene significance, this study focused on investigating the characteristic attributes of the key module MEmidnightblue. Through systematic analysis, it was found that there was a significant positive correlation between the module membership (MM) and gene significance (GS) of 3429 genes within this module, and its Pearson correlation coefficient reached 0.74 (P < 1×10 -200 )( Figure 4 D). This strong correlation indicates that the genes in the MEmidnightblue module show a highly consistent expression regulation pattern during the SAE disease course, suggesting that these genes may play a key role in the formation of the disease phenotype of SAE. Based on the MM - GS correlation analysis and phenotypic association assessment, this study identified the MEmidnightblue module as the core candidate module for SAE treatment response. This module may participate in the pathophysiological process of SAE by regulating the immune - inflammatory response pathway or neuroprotective mechanism. To further understand the molecular mechanism of this module, subsequent studies will conduct functional enrichment analysis in order to reveal its specific role pathways and molecular networks in the pathogenesis of SAE.

[0087] Figure 4 A. Gene clustering dendrogram based on the TOM distance matrix, with different colors representing the merged gene modules. B. Heatmap of the correlation between modules and phenotypic characteristics (Sham / CLP); color gradient: red indicates positive correlation, blue indicates negative correlation, and the depth of color indicates the strength of the correlation. C. Box plot of the gene significance distribution in modules: showing the significance distribution of genes in different modules; significance (P - value): P < 0.01 indicates gene significance. D. Scatter regression plot of gene membership (MM) and gene significance (GS) in the MEmidnightblue module: correlation (cor): 0.74 indicates a strong positive correlation between gene membership and gene significance; P - value: < 1e - 200 indicates a very significant correlation.

[0088] (3) Intersection analysis of WGCNA analysis and DEG characteristic gene sets

[0089] 1) Integrated analysis of gene co - expression network and differentially expressed genes:

[0090] In this study, the weighted gene co-expression network analysis (WGCNA) method was used to successfully identify a gene module MEmidnightblue that is closely related to the SAE phenotype. Through systematic analysis, it was found that this module contains a total of 3,429 characteristic genes, and the correlation coefficient between the module characteristic genes and the target phenotype reached 0.82 (p-value < 0.05), showing a significant correlation. In the differential expression analysis, a total of 84 significantly differentially expressed genes were identified, among which 75 genes showed up-regulated expression and 9 genes showed down-regulated expression. To further verify the reliability of the research results, this study performed an intersection analysis on the gene sets screened by the two methods of WGCNA and differential expression gene (DEG) analysis, and finally obtained 58 core genes ( Figure 5 A). This finding has important biological significance, indicating that although WGCNA and DEG analysis adopt different methodological frameworks, both point to the same gene set, suggesting that these core genes may play a key regulatory role in the pathological process of SAE. The research results provide an important theoretical basis and experimental evidence for further elucidating the molecular mechanism of SAE.

[0091] (2) Gene Ontology (GO) functional enrichment analysis:

[0092] Through the GO functional enrichment analysis of the 58 core genes, the research results showed that these genes showed significant enrichment characteristics at three levels: biological process (BP), cellular component (CC), and molecular function (MF) ( Figure 5 B C D). Specifically, at the biological process level, these genes showed significant enrichment in extracellular matrix metabolism (glycosaminoglycan metabolic process), regulation of angiogenesis, and immune response-related pathways ( Figure 6 D). At the cellular component level, the core genes were mainly located in structural regions such as the extracellular matrix and basement membrane ( Figure 5 C). At the molecular function level, the core genes were significantly enriched in functions such as glycosaminoglycan binding and cytokine activity. The study used circlize to display the hierarchical structure of GO terms and their enrichment degree, and "glycosaminoglycan metabolic process" occupied the largest sector in the figure (the proportion of the number of genes was 32.1%, P = 1.2×10^-6). This finding suggests that extracellular matrix remodeling may be one of the important pathological mechanisms of SAE.

[0093] (3) KEGG Pathway Enrichment Analysis:

[0094] Based on the results of KEGG pathway analysis, we deeply explored the biological networks and their functional characteristics involved by the core genes ( Figure 5 E and F). The analysis showed that the PI3K-Akt signaling pathway presented significant enrichment characteristics (FDR = 1.5×10^-5), and this pathway contained a total of 12 key genes (such as COL1A1, LAMA2, Spp1, etc.). The abnormal expression of these genes may directly affect the regulatory mechanisms of cell proliferation and survival. At the same time, the IL-17 signaling pathway also showed an obvious enrichment trend (FDR = 0.003). The participation of genes such as CXCL1 and MMP9 suggested that this pathway plays an important role in the inflammatory cascade reaction. In terms of the hematopoietic cell lineage (FDR = 0.008), the enrichment of genes such as CD34 and KIT may reflect the abnormal regulation of the immune cell differentiation process. It is worth noting that the gene enrichment ratio of the "Viral myocarditis" (FDR = 0.002) pathway was as high as 21.1%. This finding implies that there may be a potential association between SAE and immune-mediated cardiovascular damage ( Figure 5 F). Through the statistical analysis of the bubble chart ( Figure 5 E), we found a significant negative correlation between the pathway gene ratio and the significance (r = -0.73, P<0.01). This result indicates that the biological specificity of the highly enriched pathways is more significant, providing an important theoretical basis for subsequent research.

[0095] Figure 5 A. Venn diagram; Blue area: Genes in the WGCNA module; Red area: DEG genes; Overlapping area: Intersection genes; 3371: Total number of genes in the WGCNA module: 3429; Total number of DEG genes: 84; Number of intersection genes: 58. B. Enrichment analysis circle chart; Color: Represents the -log10 transformation of the P value; Size: Represents the number of enriched genes; C. GO enrichment bubble chart; Color: Represents the -log10 transformation of the P value; Size: Represents the number of enriched genes; D. GO enrichment bar chart; Color: Represents the -log10 transformation of the P value; Biological Process, Cellular Component, Molecular Function. E. KEGG enrichment bar chart; Color: Represents the -log10 transformation of the P value; F. KEGG enrichment bar chart. Color gradient: From blue (low significance) to red (high significance).

[0096] (4) Screening of disease characteristic gene sets by machine learning analysis

[0097] 1) Screening of key genes for SAE by LASSO regression analysis: This study aimed to systematically identify key regulatory genes in the pathological process of systemic autoimmune arthritis (SAE). To achieve this goal, the least absolute shrinkage and selection operator (LASSO) regression model was used to screen features and select variables for 58 candidate genes. Specifically, the LASSO analysis was performed using the 'glmnet' package in the R language platform (version 4.1.8), setting the regression type to binomial distribution, and optimizing the model parameters by 10-fold cross-validation method. The analysis results showed ( Figure 6 A), as the value of the regularization parameter λ (log(λ)) gradually increased, the model complexity showed an obvious decreasing trend, and the gene coefficients gradually became sparse. Through cross-validation error (Binomial Deviance) analysis, it was found that when λ = 0.021, the model reached the optimal fitting state, and the simplified model parameter within 1 standard error range corresponding to this was λ = 0.085 ( Figure 6 B). Under the optimal model parameters, 8 key genes (Adm, Cort, Gpr34, H2-Aa, Ier3, Itgad, Matn1, Spp1) with significantly non-zero coefficients were successfully screened from the initial 58 candidate genes. The regression coefficients of these genes were all significantly greater than zero (|β| > 0), indicating their important predictive value and biological significance in the SAE phenotype classification.

[0098] 2) Screening of key genes for SAE by SVM-RFE recursive feature elimination analysis: To ensure the reliability and robustness of the screened key genes, the support vector machine recursive feature elimination (SVM-RFE) method was used for secondary screening of genes in this study. Specifically, based on the 'e1071' package (version 1.7 - 13) in the R language environment, a linear kernel support vector machine (SVM) model was constructed, and the gene features with the lowest contribution to classification were gradually eliminated through recursive iteration. During the feature selection process, systematic evaluation showed a significant dynamic relationship between model performance and the number of features. After optimization analysis, when the number of features decreased to 16, the 10-fold cross-validation accuracy of the model reached the optimal value (85%) ( Figure 6 C), and at the same time the error rate also decreased to the lowest level (15%) ( Figure 6D). Under this optimal feature set, the model retained 16 genes with significant classification value, including Sdcbp2, Ier3, Serpina3n, Glp2r, Pnpla2, Angptl4, Spp1, Ddc, Rhoj, Tnfrsf25, H2-Aa, Cd163, Itih4, Slc2a1, Cort, and Igfbp7. These genes all showed the highest weight coefficients under the L1 norm constraint ( Figure 6 D), indicating that they play a core role in classifying SAE and have important biological significance.

[0099] Figure 6 A. LASSO regression cross-validation curve: Red line: binomial deviance corresponding to the optimal λ value; Dashed line: confidence interval for λ value selection. B. LASSO coefficient shrinkage path: Different colored lines: coefficient shrinkage paths of different features; Red dashed line: coefficient shrinkage path corresponding to the optimal λ value. C. Relationship curve between the number of SVM-RFE features and cross-validation accuracy; Curve: relationship between the number of features and cross-validation accuracy; Marked points: cross-validation accuracy corresponding to the optimal number of features. D. Relationship curve between the number of SVM-RFE features and cross-validation error rate; Curve: relationship between the number of features and cross-validation error rate; Marked points: cross-validation error rate corresponding to the optimal number of features.

[0100] 5) Identification of the intersection of candidate gene Spp1 and evaluation of the diagnostic efficacy of Spp1 gene in SAE mice

[0101] 1) Joint screening and intersection identification of key genes: In this study, an integrated strategy of three machine learning methods, namely LASSO regression, SVM-RFE, and MCC scoring, was innovatively adopted to conduct multi-dimensional screening of candidate genes in a mouse model of SAE (systemic associated encephalopathy). During the data analysis process, we used the "VennDiagram" package (version 1.7.3) under the R language platform to construct a Venn diagram ( Figure 7 A) for visual analysis of the intersection relationship of the screening results of the three methods. Specifically, LASSO regression screened out 8 core genes, the SVM-RFE method retained 16 feature genes, and MCC scoring provided the top 10 important gene rankings. Through intersection analysis, it was found that only the Spp1 (Secreted Phosphoprotein 1) gene showed significant overlap in the screening results of the three methods. This finding strongly suggests that the Spp1 gene is highly consistently important in the pathological process of SAE and may play a key role in the pathogenesis of this disease.

[0102] 2) Validation of the diagnostic efficacy of the Spp1 gene: To evaluate the diagnostic potential of Spp1 in SAE mice, the classification ability of its expression level was analyzed by receiver operating characteristic (ROC) curve in this study ( Figure 7 B). 10-fold cross-validation was performed using the "pROC" package (version 1.18.5), and the results showed that the AUC value was 0.922 (95% CI = [0.719, 0.922]). At the optimal cutoff value (Cutoff = 8.846), the sensitivity reached 100%, and the specificity was 87.5%.

[0103] Figure 7 A. Venn diagram; blue area: genes screened by LASSO regression; purple area: genes screened by SVM-RFE; red area: top 10 core hub genes with MCC scores. B. ROC curve; ROC curve of the Spp1 gene; AUC: 0.922, indicating the diagnostic efficacy of the Spp1 gene; 95% CI: 0.719 - 0.922, indicating the confidence interval of the AUC value.

[0104] (6) Spp1 mRNA expression level in SAE mice

[0105] In this study, real-time fluorescence quantitative PCR (qRT-PCR) technology was used to detect the expression levels of Spp1 mRNA in the brain tissue and peripheral blood of SAE mice (1 day, 3 days, 7 days, 14 days) and sham-operated control groups.

[0106] 1) Spp1 mRNA expression level in the brain tissue of SAE mice

[0107] The qRT-PCR results showed that Spp1 mRNA presented a significant biphasic expression pattern in the brain tissue of SAE mice ( Figure 8 A):

[0108] ① Acute phase (1 - 3 days): One day after CLP, the expression level of Spp1 mRNA was significantly upregulated to 2.25 times that of the control group (CLP-D1: 2.25 ± 0.27 vs Sham: 1.00 ± 0.03; P = 0.0029), and reached the peak 3 days after the operation (CLP-D3: 2.26 ± 0.13 vs Sham: 1.03 ± 0.10; P = 0.0003).

[0109] ② Subacute phase (7 - 14 days): Seven days after the operation, the expression of Spp1 returned to the baseline level (CLP-D7: 0.86 ± 0.10 vs Sham: 0.90 ± 0.03; P = 0.431), and there was no significant difference at 14 days (CLP-D14: 1.12 ± 0.25 vs Sham: 0.95 ± 0.06; P = 0.224).

[0110] 2) Spp1 mRNA expression level in the peripheral blood of SAE mice

[0111] The qRT-PCR results showed that the expression pattern of Spp1 mRNA in the peripheral blood of SAE mice was as follows: ( Figure 8 B)

[0112] One day after CLP, the expression level of Spp1 mRNA in the peripheral blood of the sepsis-induced cognitive impairment mouse group (CLP group) was significantly upregulated to approximately 126 times that of the control group (CLP group: 132.07 ± 7.76 vs Sham group: 1.04 ± 0.08; P < 0.0001). This indicates that in the early stage after CLP, the expression level of Spp1 mRNA increased significantly in the CLP group..

[0113] On the 3rd day after CLP, although the expression level of Spp1 mRNA in the peripheral blood of the sepsis-induced cognitive impairment mouse group (CLP group) decreased compared with Day 1, it was still significantly higher than that of the Sham group, approximately 16.2 times that of the Sham group (CLP group: 17.85 ± 6.85 vs Sham group: 1.10 ± 0.07; P = 0.0142).

[0114] On the 7th day after CLP, the expression level of Spp1 mRNA in the peripheral blood of the sepsis-induced cognitive impairment mouse group (CLP group) continued to decrease, but it was still significantly higher than that of the Sham group, approximately 4 times that of the Sham group (CLP group: 3.76 ± 1.38 vs Sham group: 0.93 ± 0.13; P = 0.0233).

[0115] On the 14th day after CLP, the gap in the expression level of Spp1 mRNA between the peripheral blood of the sepsis-induced cognitive impairment mouse group (CLP group) and the Sham group further narrowed, but the CLP group was still significantly higher than the Sham group, approximately 2 times that of the Sham group (CLP group: 2.39 ± 0.59 vs Sham group: 1.07 ± 0.21; P = 0.0296).

[0116] These results indicate that the CLP surgery significantly induced an upregulation of the expression level of Spp1 mRNA in the peripheral blood of SAE mice, and this upregulation effect persisted for some time after the surgery, although it weakened over time. This significant upregulation of the Spp1 mRNA expression level may be related to the inflammatory response and tissue damage repair process induced by CLP, suggesting that the Spp1 gene may play an important role in the pathophysiological process after CLP in peripheral blood leukocytes.

[0117] Figure 8In it, the temporal changes in the expression of Spp1 mRNA in the brain tissue of A.SAE mice were detected by qRT-PCR for the expression levels of Spp1 mRNA in the small intracranium of the SAE group and the sham operation control group at different time points (1 day, 3 days, 7 days, 14 days) after CLP. B. The temporal changes in the expression of Spp1 mRNA in the peripheral blood of SAE mice. The expression levels of Spp1 mRNA in the small intracranium of the SAE group and the sham operation control group were detected by qRT-PCR at different time points (1 day, 3 days, 7 days, 14 days) after CLP. ns: Not Significan, *P<0.05, ****P<0.0001.

[0118] (7) Functional association between the distribution of SPP1 in the hippocampal subregions and the hippocampal pathological microenvironment

[0119] It is worth noting that the significantly high expression of SPP1 in the CA1, CA3 and DG subregions (Δ fluorescence intensity > 50%) is highly correlated with the characteristics of the hippocampal pathological microenvironment. Combining previous studies, as a ligand of integrin αvβ3 (CD61), the regional enrichment of SPP1 may activate downstream signaling pathways, regulate abnormal synaptic pruning mediated by microglia, and thus exacerbate related cognitive impairments.

[0120] Figure 9 A. Immunofluorescence staining of SPP1 in the hippocampal CA1 (Cornu Ammonis 1), CA3 (Cornu Ammonis 3) and dentate gyrus (DG) regions of 7-day-old mice was detected (A: Scarbar = 100 pm). B. Statistical analysis of SPP1 immunofluorescence in the hippocampal CA1; C. Statistical analysis of SPP1 immunofluorescence in CA3; D. Statistical analysis of SPP1 immunofluorescence in the dentate gyrus. Sham group: sham operation control group; CLP group: cecal ligation and puncture SAE group; n = 3 in each group; paired t-test was used for comparison between groups. *P < 0.05.

[0121] (8) Persistent high expression of SPP1 protein in SAE mice

[0122] To deeply explore the temporal expression characteristics of SPP1 protein in the pathological process of SAE, Western blot technology was used in this study to systematically analyze the expression levels of SPP1 protein in the hippocampal tissues of SAE mice at different time points (1 day, 3 days, 7 days, 14 days) after CLP. Figure 10A). To establish a reliable control benchmark, a sham operation control group (Sham group, n = 3) was set up in the experiment. In the data normalization process, all protein expression levels were quantified by gray value analysis and normalized using the Sham group as the baseline (set to 1.0 ± 0.14). To ensure the scientificity and reliability of the statistical results, one-way ANOVA combined with Dunnett's multiple comparison test was used for statistical analysis, and the significance level was set at P < 0.05. Through the above rigorous experimental design and analysis methods, this study provided a reliable experimental basis for revealing the dynamic change law of SPP1 protein in the course of SAE.

[0123] 1) Temporal expression dynamics of SPP1 protein

[0124] The quantitative results of Western blot showed that the expression of SPP1 protein in the hippocampus of the SAE group presented a significant continuous up-regulation pattern ( Figure 10 B):

[0125] ① On the 1st day after surgery, the expression level of SPP1 increased significantly to 2.06 ± 0.19 times that of the Sham group (mean difference: -0.4007; 95% confidence interval: -0.7938 to -0.0076; P = 0.045). This result indicates that SPP1 is rapidly activated in the acute stress response of SAE.

[0126] ② On the 3rd day after surgery, the expression level of SPP1 increased significantly to 2.45 ± 0.20 times (mean difference: -0.5469; 95% confidence interval: -0.9400 to -0.1538; P = 0.006).

[0127] ③ On the 7th day after surgery, the expression of SPP1 reached its peak, at 2.59 ± 0.21 times (mean difference: -0.5971; 95% confidence interval: -0.9902 to -0.2041; P = 0.003).

[0128] ④ On the 14th day after surgery, SPP1 still maintained a high expression level (2.55 ± 0.20 times, mean difference: -0.5844; 95% confidence interval: -0.9775 to -0.1914; P = 0.004).

[0129] 2) Pathological significance of continuous activation of SPP1

[0130] The experimental data were statistically analyzed by one-way ANOVA. The results showed significant differences among different experimental groups (F(4,15) = 6.046; P = 0.0042), indicating high statistical significance. Further analysis showed that SPP1 protein was continuously highly expressed during the course of SAE. During the observation period from 1 to 14 days after surgery, its expression level remained above 2.0 times the basal value.

[0131] Figure 10 A Western Blot analysis: Representative Western blot bands showed the expression of SPP1 protein and the internal reference α-tubulin at different time points (Sham, CLP 1d, CLP 3d, CLP 7d, CLP 14d). Protein sizes: The SPP1 protein was approximately 32 kDa, and the α-tubulin protein was approximately 55 kDa. B. Relative expression of SPP1 protein: Quantitative histogram of the relative expression of SPP1 protein, normalized to the expression of α-tubulin. Sham group: Sham operation control group; CLP group: Cecal ligation and puncture group. Time points: 1 day (1d), 3 days (3d), 7 days (7d), 14 days (14d) after surgery. α-tubulin: Used as an internal reference protein to normalize the expression of SPP1 protein. Western blot: An experimental technique used to detect the expression of specific proteins. One-way ANOVA, *P < 0.05, **P < 0.01.

[0132] (9) Co-localization of hippocampal vascular endothelial CD31 and SPP1 suggests the migration of peripheral SPP1 to the central nervous system. To explore the cellular source of SPP1 protein in the hippocampus of SAE mice and its interaction with the vascular system, this study used double immunofluorescence labeling technology to stain the hippocampus of mice 3 days after CLP (the peak period of SAE). The SPP1 protein was labeled with red fluorescence (Cy3 channel), the vascular endothelial cell-specific marker CD31 was labeled with green fluorescence (FITC channel), and tissue localization was carried out in combination with DAPI nuclear staining (blue) Figure 11 A). The SPP1 protein was mainly localized in the luminal membrane and adjacent basement membrane regions of CD31-positive vascular endothelial cells (indicated by arrows Figure 11 B). Notably, SPP1 also showed punctate aggregation in the cytoplasm of vascular endothelial cells. Combining the single-cell localization results of the Spp1 gene, it can be inferred that plasma SPP1 enters the brain parenchyma through the leaky blood-brain barrier (BBB) or active transport by endothelial cells; the above results indicate that the abnormal accumulation of SPP1 in the hippocampus of SAE mice is closely related to the vascular system.

[0133] Figure 11.A. shows the results of double immunofluorescence staining of SPP1 protein (red) and CD31 (green) in the hippocampus of SAE mice. DAPI (blue) is used to label cell nuclei. B. White arrows indicate the co-localization area of SPP1 protein and CD31.

[0134] (10) Exogenous SPP1 tracing experiment confirms the peripheral-central trans-barrier transport pathway

[0135] To clarify the transport pathway of peripherally-derived SPP1 to the central nervous system in SAE, in this study, an exogenous recombinant His-tagged SPP1 protein (His-SPP1) tracing technique was used, combined with a macrophage conditional knockout Spp1 transgenic mouse model, to analyze the trans-barrier transport characteristics of peripheral SPP1 ( Figure 12 ).

[0136] 1) Experimental method: Macrophage-specific Spp1 knockout mice (SPP1 KO group) and wild-type controls (Loxp group) received CLP surgery. In the experimental group (His-SPP1 group), His-tagged recombinant SPP1 protein (10 μg / g body weight, dissolved in normal saline) was injected intraperitoneally, and the control group (NS group) was injected with an equal volume of normal saline; hippocampal tissues were collected 24 hours after surgery, fixed with 4% paraformaldehyde, and then prepared into coronal sections (thickness 10–20 μm). Immunofluorescence staining was performed using an anti-His tag antibody to localize and quantify the distribution of exogenous SPP1 in the hippocampus; the fluorescence signal intensity was quantitatively analyzed using ImageJ software.

[0137] 2) The results of immunofluorescence staining showed ( Figure 12 -A and B): Trans-barrier accumulation of His-SPP1: In the hippocampus of mice in the His-SPP1 group, significantly aggregated His-positive signals (green fluorescence) were visible, showing a dense granular distribution; Fluorescence intensity quantification: The fluorescence intensity in the hippocampus of the His-SPP1 group (40.75 ± 2.24) was 3.34 times higher than that in the NS group (12.18 ± 1.83) (t = 12.64, df = 10, P = 0.0002), confirming that exogenous SPP1 can be efficiently transported from the peripheral circulation to the central tissue;

[0138] Verification of transport specificity: In SPP1 KO mice, in the background of the absence of endogenous SPP1 expression, the accumulation of exogenous His-SPP1 further excluded the interference of endogenous proteins, clearly indicating that the tracer protein was derived from peripheral injection.

[0139] Figure 12Immunofluorescence staining of A shows the expression of SPP1 in the hippocampus of the SAE model; (NS): Immunofluorescence staining results in the hippocampus of mice in the control group (non-specific group). DAPI (blue) is used to label the cell nucleus, His (green) is used to label the His-tagged SPP1 protein, and the merged image shows the cellular localization of the His-SPP1 protein. His-SPP1): Immunofluorescence staining results in the hippocampus of mice in the experimental group (His-SPP1 group). It can be observed that compared with the control group, the expression of His-SPP1 is significantly increased, manifested as strong fluorescence signals. B. Quantitative analysis of the immunoreactivity of the His-tagged SPP1 protein. ***P < 0.001.

[0140] (11) Conditional knockout of Spp1 in peripheral macrophages improves SAE

[0141] (1) Improvement effect of conditional knockout of Spp1 in peripheral macrophages on sepsis-associated cognitive dysfunction

[0142] To systematically evaluate the regulatory effect of Spp1 deficiency in peripheral macrophages on cognitive function in SAE, in this study, the cognitive behavioral phenotypes of wild-type (Loxp group) and macrophage-specific Spp1 knockout (SPP1 KO group) mice after CLP were dynamically analyzed through the open field test (OFT), novel object recognition test (NOR), and Y-maze test (YM).

[0143] 1) Baseline assessment of motor function (OFT experiment)

[0144] On the 7th day after CLP, the basic motor ability of mice was detected by OFT ( Figure 13 A and B). The results showed that there was no significant difference in the total movement distance between the Spp1 KO group (976.6 ± 267.52 cm) and the Loxp group (752.15 ± 111.65 cm) (t = 1.957, df = 14, P = 0.0723); Behavioral interference exclusion: The above results suggest that CLP surgery and Spp1 knockout did not significantly affect the motor ability of mice, and subsequent cognitive behavioral differences can be attributed to specific changes in neurological function.

[0145] 2) Dynamic analysis of novel object recognition ability (NOR experiment)

[0146] On the 7th day after surgery ( Figure 13C and D): Exploration time of novel object: The Spp1 KO group (1.052 ± 0.92 s) was significantly increased compared with the Loxp group (0.12 ± 0.16 s) (t = 2.32, df = 14, P = 0.0359); Recognition index: The recognition index of the Spp1 KO group (19.49 ± 3.36%) was 8.6 times higher than that of the Loxp group (3.94 ± 2.59%) (t = 6.914, df = 14, P < 0.0001).

[0147] On the 14th day after surgery ( Figure 14 A and B): Exploration time of novel object: The Spp1 KO group (2.45 ± 1.55 s) was significantly higher than the Loxp group (0.135 ± 0.30 s) (t = 3.36, df = 13, P = 0.0051); Recognition index: The Spp1 KO group (63.53 ± 31.64%) was 22 times higher than that of the Loxp group (2.84 ± 4.07%) (t = 4.832, df = 13, P = 0.0004).

[0148] 3) Evaluation of spatial memory function (YM test)

[0149] On the 7th day after surgery ( Figure 13 E and F): Total number of entries into the arms: The Spp1 KO group (28.7 ± 5.77 times) was significantly increased compared with the Loxp group (18.67 ± 9.428 times) (t = 2.523, df = 14, P = 0.0271); Spontaneous alternation rate: The Spp1 KO group (62.94 ± 6.99%) was significantly higher than the Loxp group (51.64 ± 3.15%) (t = 3.47, df = 14, P = 0.00038).

[0150] On the 14th day after surgery ( Figure 14 C and D): Total number of entries into the arms: The Spp1 KO group (31.4 ± 6.83 times) was significantly increased compared with the Loxp group (18.16 ± 10.38 times) (t = 2.781, df = 13, P = 0.0140); Spontaneous alternation rate: The Spp1 KO group (64.29 ± 11.43%) was significantly higher than the Loxp group (45.41 ± 14.45%) (t = 2.62, df = 13, P = 0.0211).

[0151] Figure 13Among them, representative locomotor trajectories of Spp1 conditional knockout mice after A.CLP in the OFT. B. Bar graph of the total locomotor distance of mice in the OFT on the 7th day after modeling. C. Performance in the novel object recognition (NOR) experiment. D. Bar graph of the novel object recognition time and novel object recognition index. E. Trajectory graph in the Y maze (YM) experiment. F. Bar graph of the total number of arm entries and spontaneous alternation rate in the Y maze experiment. Spp1 KO group: septic mice with conditional knockout of Spp1 in peripheral macrophages; Loxp group: septic mice without knockout of the Spp1 gene in macrophages. N = 10 in the Spp1 KO group and N = 6 in the Loxp group. Unpaired t-tests were used for between-group comparisons. ns: Not Significan, *P<0.05, **P<0.01.

[0152] (15) Conditional knockout of SPP1 in peripheral macrophages reduces hippocampal SPP1 protein expression

[0153] To clarify the specific contribution of peripherally derived macrophage SPP1 to central SPP1 protein expression in SAE, in this study, we systematically compared the expression levels of SPP1 in the hippocampal regions of wild-type (Loxp group), macrophage-specific SPP1 knockout (SPP1 KO group), and sham-operated control (Sham group) mice after CLP by immunofluorescence staining and Western Blot techniques ( Figure 15 ).

[0154] 1) Quantitative analysis of SPP1 protein expression by immunofluorescence staining

[0155] The results of immunofluorescence staining showed ( Figure 14 A and B): Differences between groups after CLP: The immunofluorescence intensity of SPP1 in the hippocampal region of the SPP1 KO group (18.04 ± 1.01) was significantly reduced by 50.96% compared with that of the Loxp group (36.79 ± 4.81) (t = 3.882, df = 8, P = 0.0215); Comparison with the Sham group: There was no significant difference in the SPP1 expression level between the Spp1 KO group and the Sham group (16.72 ± 2.14) (t = 1.764, df = 8, P = 0.054), indicating that conditional knockout of peripheral macrophage SPP1 can restore central SPP1 to the physiological baseline level.

[0156] 2) Verification analysis of SPP1 protein expression by Western Blot

[0157] The Western Blot results further verified the above findings ( Figure 15C and D): SPP1 protein expression level: The relative expression level of SPP1 in the hippocampus of the SPP1 KO group (0.52 ± 0.12) was significantly decreased by 47.47% compared with that of the Loxp group (0.99 ± 0.11) (t = 3.612, df = 8, P = 0.0154); Cross-model consistency: The co-analysis of immunofluorescence and Western Blot showed that the deletion of SPP1 in peripheral macrophages could effectively inhibit the abnormal accumulation of central SPP1 induced by sepsis.

[0158] Figure 15 A. Immunofluorescence staining showed the immunofluorescence staining results of DAPI (blue), SPP1 (green) and their merged images. These images demonstrated the expression and localization of SPP1 in different groups. B. Histogram of the quantitative analysis results of SPP1 immunoreactivity, showing the differences in SPP1 expression levels among the Sham group, Spp1 KO group and LoxP group. C. Western Blot bands of SPP1 and internal reference proteins (such as GAPDH); D. Statistical analysis data of Western Blot relative expression levels were all expressed as mean ± standard deviation. *P < 0.05, ns: Not Significan.

Claims

1. A biomarker for detecting septic encephalopathy, characterized in that The biomarker is SPP1.

2. Use of the biomarker of septic encephalopathy according to claim 1 in the preparation of a product for diagnosing septic encephalopathy.

3. A method for detecting a biomarker of sepsis encephalopathy, characterized in that, Detecting the expression level of Spp1 in peripheral macrophages, detecting the expression level of SPP1 in plasma, and / or detecting the transcriptional level of the Spp1 gene.

4. The detection method according to claim 8, characterized in that, Directly detecting the SPP1 protein expression level by ELISA, immunoblotting or immunohistochemistry methods or detecting the transcriptional level of the Spp1 gene by qRT-PCR to evaluate the Spp1 expression level.

5. Use of an Spp1 gene, an antibody that inhibits or binds to the Spp1 protein in the preparation of a drug for treating septic encephalopathy.

6. The application according to claim 5, characterized in that, The drug treats septic encephalopathy by reducing the phagocytosis of neuronal synaptic proteins by microglia.

7. The application according to claim 6, wherein Reducing the transfer of peripheral blood SPP1 protein into microglia in the hippocampal region of the brain to reduce the activity degree of the microglia.

8. The use according to claims 4-6, wherein the inhibitor is at least one of a specific inhibitor against the Spp1 gene, miRNA, RNAi, and ribozyme, or a small molecule inhibitor against the Spp1 protein.

9. A pharmaceutical composition, characterized in that, The pharmaceutical composition comprises an antibody that binds to the Spp1 protein, an SPP1 inhibitor, and the Spp1 gene.

10. The pharmaceutical composition according to claim 9, characterized in that, The pharmaceutical composition further comprises a pharmaceutically acceptable excipient; preferably, the excipient comprises any one or a combination of at least two of a carrier, a diluent, an emulsifier, a solubilizer, a solubilizing agent, an osmotic pressure regulator, a coating material, a coloring agent, a pH regulator, an antioxidant, a bacteriostatic agent, or a buffer.

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